def tattoo_detection(net, image_name, args):
"""Detect object classes in an image using pre-computed object proposals."""
im_in = cv2.imread(image_name)
if im_in is None:
print('cannot open %s for read' % image_name )
exit(-1)
rows,cols = im_in.shape[:2]
print([rows,cols])
scale=1.0
if rows >= cols:
scale = float(args.longdim) / float(rows)
im = cv2.resize( im_in, (int(0.5 + float(cols)*scale), args.longdim) )
else:
scale = float(args.longdim) / float(cols)
im = cv2.resize( im_in, (args.longdim, int(0.5 + float(rows)*scale)) )
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
seconds = '%.3f' % timer.total_time
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
max_scores = scores.max(axis=0)
print(max_scores)
print(boxes.shape)
# Visualize detections for each class
CONF_THRESH = args.threshold
NMS_THRESH = args.nms_thresh
tattoo_dets=[]
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= CONF_THRESH)[0]
dets_filter = dets[inds]
vis_detections(im, cls, dets_filter, thresh=CONF_THRESH)
if cls == 'tattoo' and len(dets_filter)>0:
plt.savefig(os.path.join(args.output, os.path.splitext(os.path.basename(image_name))[0] + '_det.png'))
tattoo_dets = dets_filter
if args.inspect == 'v':
plt.show()
plt.clf()
return tattoo_dets, max_scores, seconds, scale
def detect_bboxes(net, im_names, subset_classes):
"""Detect object classes in an image using pre-computed object proposals."""
df = cnn_utils.create_bbox_data_frame(with_object_index=False)
for im_name in im_names:
print '~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'
print 'Demo for {}'.format(im_name)
# Load the input image.
im_file = os.path.join(FLAGS.data_dir, 'images', im_name)
im = cv2.imread(im_file)
im_size_x = im.shape[1]
im_size_y = im.shape[0]
# Detect all object classes and regress object bounds.
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(
timer.total_time, boxes.shape[0])
# Detect for each class
for subset_cls_ind in range(len(class_names_to_be_detected)):
cls = class_names_to_be_detected[subset_cls_ind]
try:
cls_ind = CLASSES.index(cls)
except:
print('error: class does not exist in training data: '
'{0}'.format(cls))
exit(-1)
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, FLAGS.nms_thresh)
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= FLAGS.conf_thresh)[0]
if len(inds) > 0:
print ('{} {}(s) are detected.'.format(len(inds), cls))
for i in inds:
# ['image_name', 'class_index', 'x1', 'y1', 'x2', 'y2', 'score']
x1 = dets[i, 0]
y1 = dets[i, 1]
x2 = dets[i, 2]
y2 = dets[i, 3]
score = dets[i, -1]
if FLAGS.ignore_bbox_on_boundary:
# Ignore bounding boxes on the frame boundary.
if x1 <= 0 or x2 >= (im_size_x - 1) or \
y1 <= 0 or y2 >= (im_size_y - 1):
continue
# Append a row.
df.loc[len(df)] = [
im_name, subset_cls_ind, x1, y1, x2, y2, score]
return df
def get_ohem_minibatch(loss, rois, labels, bbox_targets=None,
bbox_inside_weights=None, bbox_outside_weights=None):
"""Given rois and their loss, construct a minibatch using OHEM."""
loss = np.array(loss)
if cfg.TRAIN.OHEM_USE_NMS:
# Do NMS using loss for de-dup and diversity
keep_inds = []
nms_thresh = cfg.TRAIN.OHEM_NMS_THRESH
source_img_ids = [roi[0] for roi in rois]
for img_id in np.unique(source_img_ids):
for label in np.unique(labels):
sel_indx = np.where(np.logical_and(labels == label, \
source_img_ids == img_id))[0]
if not len(sel_indx):
continue
boxes = np.concatenate((rois[sel_indx, 1:],
loss[sel_indx][:,np.newaxis]), axis=1).astype(np.float32)
keep_inds.extend(sel_indx[nms(boxes, nms_thresh)])
hard_keep_inds = select_hard_examples(loss[keep_inds])
hard_inds = np.array(keep_inds)[hard_keep_inds]
else:
hard_inds = select_hard_examples(loss)
blobs = {'rois_hard': rois[hard_inds, :].copy(),
'labels_hard': labels[hard_inds].copy()}
if bbox_targets is not None:
assert cfg.TRAIN.BBOX_REG
blobs['bbox_targets_hard'] = bbox_targets[hard_inds, :].copy()
blobs['bbox_inside_weights_hard'] = bbox_inside_weights[hard_inds, :].copy()
blobs['bbox_outside_weights_hard'] = bbox_outside_weights[hard_inds, :].copy()
return blobs
def detect(net, im):
""" """
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
res = 0
global CLASSES
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
res += vis_detections(im, cls, dets, thresh=CONF_THRESH)
return im,res
def reduce_boxes(self, scores, boxes):
"""
Reduce the result boxes
"""
box_classes = []
box_scores = np.array([], dtype="float32")
for j in xrange(1, self.num_classes+1):
# single-class NMS
inds = np.where(scores[:, j] > self.score_thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, self.IoU_thresh, self.force_cpu)
# use vstack or list
box_classes += [j] * len(keep)
cls_dets = cls_dets[keep, :]
box_scores = np.append(box_scores, cls_scores[keep])
if j == 1:
all_dets = cls_dets
else:
all_dets = np.vstack((all_dets, cls_dets))
box_classes = np.array(box_classes, dtype=int)
# Limit to max_per_image detections *over all classes*
if len(box_classes) > self.max_per_image:
indexes = np.argsort(-box_scores)[:self.max_per_image]
all_dets = all_dets[indexes, :]
box_classes = box_classes[indexes]
return (box_classes, all_dets)
def demo_detect(net, filename, blob_name='feat', threshold=0.5):
"""Detect persons in a gallery image and extract their features
Arguments:
net (caffe.Net): trained network
filename (str): path to a gallery image file (jpg or png)
blob_name (str): feature blob name. Default 'feat'
threshold (float): detection score threshold. Default 0.5
Returns:
boxes (ndarray): N x 5 detected boxes in format [x1, y1, x2, y2, score]
features (ndarray): N x D features matrix
"""
im = cv2.imread(filename)
boxes, scores, feat_dic = _im_detect(net, im, None, [blob_name])
j = 1 # only consider j = 1 (foreground class)
inds = np.where(scores[:, j] > threshold)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j*4:(j+1)*4]
boxes = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(boxes, cfg.TEST.NMS)
boxes = boxes[keep]
features = feat_dic[blob_name][inds][keep]
if boxes.shape[0] == 0:
return None, None
features = features.reshape(features.shape[0], -1)
return boxes, features
def detect_image(net, im):
"""Detect object classes in an image using pre-computed object proposals."""
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print(str(current_process().index)+' Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
# CONF_THRESH = 0.0
CONF_THRESH = 0.4
NMS_THRESH = 0.1
person_idx = CLASSES.index('person')
person_boxes = boxes[:, 4*person_idx:4*(person_idx + 1)]
person_scores = scores[:, person_idx]
person_dets = np.hstack((person_boxes,
person_scores[:, np.newaxis])).astype(np.float32)
person_keep = nms(person_dets, NMS_THRESH)
person_dets = person_dets[person_keep, :]
# inds = np.where(dets[:, -1] >= CONF_THRESH)[0]
person_dets = person_dets[np.where(person_dets[:, -1] >= CONF_THRESH)]
return person_dets
def demo(net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(FLAGS.data_dir, 'images', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
print ('Target class: {}'.format(FLAGS.target_class))
# Visualize detections for each class
for cls_ind, cls in enumerate(CLASSES[1:]):
#
if cls != FLAGS.target_class:
continue
#
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, FLAGS.nms_thresh)
dets = dets[keep, :]
write_cropped_images(im, image_name, cls, dets, thresh=FLAGS.conf_thresh)
def demo(sess, net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
#im_file = os.path.join('/home/corgi/Lab/label/pos_frame/ACCV/training/000001/',image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections(im, cls, dets, ax, thresh=CONF_THRESH)
def demo (net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def clean_detections(scores, boxes, thresh = CONF_THRESH):
"""Clean up scores and boxes and return the interesting object in a handy format."""
for detection_index in range(len(scores)):
if scores[detection_index][0] > (1-CONF_THRESH):
continue
for cls_ind, cls in enumerate(CLASSES[1:]): #There is propably a better way to do this.
#print cls_ind, cls
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
detections = []
inds = np.where(dets[:, -1] >= thresh)[0]
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
detections.append(({"person":score}, tuple(bbox)))
return detections
def demo(net, im, return_boxes):
"""Detect object classes in an image using pre-computed object proposals."""
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
classes = {}
for cls_ind, cls in enumerate(CLASSES[1:]):
try:
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
bboxes = vis_detections(im, cls, dets, return_boxes, thresh=CONF_THRESH)
classes[cls] = bboxes
except Exception as e:
continue
if not return_boxes:
cv2.imshow("image", im)
return classes
def apply_nms(all_boxes, thresh):
"""
Apply non-maximum suppression to all predicted boxes output by the
test_net method.
"""
num_classes = len(all_boxes)
num_images = len(all_boxes[0])
nms_boxes = [[[] for _ in range(num_images)]
for _ in range(num_classes)]
for cls_ind in range(num_classes):
for im_ind in range(num_images):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
# CPU NMS is much faster than GPU NMS when the number of boxes
# is relative small (e.g., < 10k)
# TODO(rbg): autotune NMS dispatch
keep = nms(dets, thresh, force_cpu=True)
if len(keep) == 0:
continue
nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
return nms_boxes
def demo(net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.ROOT_DIR, 'data', 'demo', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background\
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
order = cls_scores.argsort()[::-1]
sorted_dets = dets[order, :]
keep = nms(dets, NMS_THRESH)
with open('/home/xyy/Desktop/doing/Object Detection/py-faster-rcnn/test_python.txt','w') as f:
dets = dets[keep, :]
for i in dets:
for j in i:
f.write(str(j)+ ' ')
f.write('\n')
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def demo(net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.7
NMS_THRESH = 0.3
json_data_list = []
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
bbox, score = vis_detections(im, cls, dets, thresh=CONF_THRESH)
if score:
json_data_list.append({"class":cls,
'bbox':bbox,
'score':score})
if len(json_data_list):
f = open("result/"+image_name+".json", "w")
json.dump(json_data_list, f, indent=2)
def demo (net, imagePathName, scoreThreshold):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im = cv2.imread(imagePathName)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
debug('Object detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))
# Visualize detections for each class
path, imageFilename = os.path.split(imagePathName)
catDir = os.path.split(path)[-1]
imageName = catDir + '/' + imageFilename
for i, cls in enumerate(CLASSES[1:]):
i += 1 # because we skipped background
cls_boxes = boxes[:, 4 * i:4 * (i + 1)]
cls_scores = scores[:, i]
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESHOLD)
dets = dets[keep, :]
vis_detections(im, cls, imageName, dets, scoreThreshold)
def produce(self, ip): scores, bbox = im_detect(self.net_, ip) #Find the top class for each box bestClass = np.argmax(scores,axis=1) bestScore = np.max(scores, axis=1) allDet = edict() for cl in [self.prms_.targetClass]: clsIdx = self.cls_.index(cl) #Get all the boxes that belong to the desired class idx = bestClass == clsIdx clScore = bestScore[idx] clBox = bbox[idx,:] #Sort the boxes by the score sortIdx = np.argsort(-clScore) topK = min(len(sortIdx), self.prms_.topK) sortIdx = sortIdx[0:topK] #Get the desired output clScore = clScore[sortIdx] clBox = clBox[sortIdx] clBox = clBox[:, (clsIdx * 4):(clsIdx*4 + 4)] #Stack detections and perform NMS dets=np.hstack((clBox, clScore[:,np.newaxis])).astype(np.float32) keep = nms(dets, self.prms_.nmsThresh) dets = dets[keep, :] #Only keep detections with high confidence inds = np.where(dets[:, -1] >= self.prms_.confThresh)[0] allDet[cl] = copy.deepcopy(dets[inds]) return allDet
def detect_person(net, im,cls_ind=1,cls='person',CONF_THRESH = 0.8):
"""Detect object classes in an image using pre-computed object proposals."""
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
NMS_THRESH = 0.3
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
# Filtering by confidence threshold as well
keep = [ind for ind in keep if cls_scores[ind]>CONF_THRESH]
if (len(keep)>1):
sizes = np.zeros((len(keep),))
for ind,curr_ind in enumerate(keep):
bbox = dets[curr_ind,:4]
sizes[ind] = (bbox[3]-bbox[1])*(bbox[2]-bbox[0])
# Retain only the biggest bounding box
keep = keep[np.argmax(sizes)]
dets = dets[keep, :]
return (dets.reshape(1,-1),cls_scores[keep])
def _post_process(self, scores, boxes):
obj_list = []
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, self.nms_thresh)
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= self.conf_thresh)[0]
if len(inds) == 0:
continue
for i in inds:
obj = msg.object()
obj.class_name = cls
obj.score = dets[i, -1]
bbox = dets[i, :4]
obj.region.x_offset = bbox[0]
obj.region.y_offset = bbox[1]
obj.region.width = bbox[2] - bbox[0]
obj.region.height = bbox[3] - bbox[1]
obj.region.do_rectify = False
obj_list.append(obj)
return obj_list
def detect_objects(imgpath):
"""Detect object classes in an image using pre-computed object proposals."""
print("in detect object")
# Load the demo image
im_file = os.path.join(imgpath)
im = cv2.imread(im_file)
print("read image")
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
print("im_detect")
scores, boxes = im_detect(app.config['net'], im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
results = dict()
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
results[cls] = detect_positions(im, cls, dets, thresh=CONF_THRESH)
return results
def detect(self, image):
'''
:param image: Image from which the objects should be detected
:param CONF_THRESH: list of confidence threshold for each category. if None or empty 0.7 will be the threshold of each category
if non-empty but zero for some of the entries then 0.7 will be threshold for zero threshold value categories.
:param NMS_THRESH: bounding box threshold, lower means less repetition and higher with high repetition
:return: returns list of tuples of bounding box and category name as ((x1,y1,x2,y2),cls_name)
'''
start = time.time()
bbox_class_list = []
scores, boxes = im_detect(self.model, image)
for cls_ind, (cls, threshold) in enumerate(zip(self.cat, self.CONF_THRESHOLD)):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, self.NMS_THRESHOLD)
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= threshold)[0]
for i in inds:
# x1,y1,x2,y2 = dets[i,:-1]
bbox_class_list.append(
{'bbox': dets[i, :-1].tolist(), 'category': cls, 'confidence': float(dets[i, -1])})
end = time.time()
return (bbox_class_list, end - start)
def demo(net, image_path):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im = cv2.imread(image_path)
# Detect all object classes and regress object bounds
started = time()
scores, boxes = im_detect(net, im)
elapsed = time() - started
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(elapsed, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def get_detections(net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = image_name#os.path.join(cfg.DATA_DIR, 'demo', image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
#timer = Timer()
#timer.tic()
scores, boxes, pose_a, pose_e = im_detect(net, im)
#timer.toc()
#print ('Detection took {:.3f}s for '
# '{:d} object proposals').format(timer.total_time, boxes.shape[0])
#print "a=%s, e=%s"%(5*pose_a, 5*pose_e)
# Visualize detections for each class
#CONF_THRESH =0.25#0.75
#print 'threashold: {}'.format(CONF_THRESH)
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = np.hstack((cls_boxes,
5*pose_a[:,np.newaxis], 5*pose_e[:,np.newaxis], cls_scores[:, np.newaxis])).astype(np.float32)
dets = dets[keep, :]
#print "a=%s, e=%s"%(5*pose_a[keep], 5*pose_e[keep])
return dets
def apply_nms(all_boxes, thresh):
"""Apply non-maximum suppression to all predicted boxes output by the
test_net method.
"""
num_classes = len(all_boxes)
num_images = len(all_boxes[0])
nms_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(num_classes)]
for cls_ind in xrange(num_classes):
if cls_ind ==0:
thresh = 0.3
else:
thresh =0.7
for im_ind in xrange(num_images):
dets = all_boxes[cls_ind][im_ind]
if dets == []:
continue
keep = nms(dets, thresh)
if len(keep) == 0:
continue
nms_boxes[cls_ind][im_ind] = dets[keep, :].copy()
return nms_boxes
def get_topK_boxes(scores, boxes, K):
keep_boxes = []
keep_scores = []
keep_class = []
for cls_ind, cls in enumerate(CLASSES):
if cls_ind == 0:
continue
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
keep_boxes.extend(cls_boxes[keep, :])
keep_scores.extend(cls_scores[keep])
keep_class.extend([cls_ind] * len(keep))
mat_scores = np.array(keep_scores)
mat_classes = np.array(keep_class)
mat_boxes = np.array(keep_boxes, dtype='int16')
# get top K
order = np.argsort(keep_scores)[::-1]
keep = order[0:K]
return (mat_boxes[keep, :], mat_scores[keep], mat_classes[keep])
def demo(net, im_file):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image as gray scale
gim = cv2.imread(im_file, flags= cv2.CV_LOAD_IMAGE_GRAYSCALE)
# convert to rgb repeated in each channel
im = cv2.cvtColor(gim, cv2.COLOR_GRAY2BGR)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def test_single_frame(sess, net, image_name, mask, force_cpu, output_dir):
"""Detect object classes in an image using pre-computed object proposals."""
#********************
# Need change here
#********************
im_file = os.path.join(cfg.DATA_DIR, 'test/images/', image_name)
#im_file = os.path.join('/home/corgi/Lab/label/pos_frame/ACCV/training/000001/',image_name)
im_bgr = cv2.imread(im_file)
im = np.zeros((im_bgr.shape[0], im_bgr.shape[1], 4))
im[:,:,0:3] = im_bgr
im[:,:,3] = mask
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
score, label, box, mask = im_detect(sess, net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, label.shape[0])
# Visualize detections for each class
im_rgb = im_bgr[:, :, (2, 1, 0)]
im_mask = np.zeros(im_rgb.shape).astype(im_rgb.dtype)
fig, ax = plt.subplots(figsize=(12, 12))
# ax.imshow(im_rgb, aspect='equal')
CONF_THRESH = 0.85
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
i = np.where(label==cls_ind)[0]
cls_score = score[i]
cls_box = box[i, :]
cls_mask = mask[i, :, :]
if cfg.DEBUG:
print 'i: '
print i.shape
print 'cls_score: '
print cls_score
print 'box.shape: '
print box.shape
print 'cls_box shape: '
print cls_box.shape
dets = np.hstack((cls_box,
cls_score[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH, force_cpu)
dets = dets[keep, :]
segs = cls_mask[keep, :, :]
print ('After nms, {:d} object proposals').format(dets.shape[0])
im_mask = vis_detections(im_rgb, im_mask, cls, dets, segs, ax, thresh=CONF_THRESH)
# plt.savefig(os.path.join(output_dir, 'box_'+image_name))
#im2 = cv2.imread(os.path.join(output_dir,'box_'+image_name))
im_rgb += im_mask/2
im_mask_grey = cv2.cvtColor(im_mask, cv2.COLOR_RGB2GRAY)
im_mask_grey[np.where(im_mask_grey!=0)] = 255
cv2.imwrite(os.path.join(output_dir,'output_'+image_name), im_rgb[:,:,(2,1,0)])
cv2.imwrite(os.path.join(output_dir,'mask_'+image_name), im_mask_grey)
return im_mask_grey
def detect(file_path, NMS_THRESH = 0.3):
im = cv2.imread(file_path)
scores, boxes = im_detect(net, im)
cls_scores = scores[: ,1]
cls_boxes = boxes[:, 4:8]
dets = np.hstack((cls_boxes,cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
return dets[keep, :]
def demo(net, im_file, output_file):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.3 #0.8
NMS_THRESH = 0.3 #0.3
# Write output to file
output = open(output_file, 'w')
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
cls_boxes = cls_boxes.transpose()
for box_ind, box in enumerate(cls_boxes):
output.write(CLASSES[cls_ind] + '\n')
output.write(str(cls_scores[box_ind]) + '\n')
output.write(' '.join([str(x) for x in cls_boxes[:,box_ind]]) + '\n')
output.close()
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections(im, cls, dets, thresh=CONF_THRESH)
def detectionMethod(self, im, im_copy, frame_gray, net, p, detectedItem, colorScheme):
scores, boxes = im_detect(net, im)
NMS_THRESH = 0.3
detectedItemsInThisFrame = []
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32) #stacks them together
keep = nms(dets, NMS_THRESH) #Removes overlapping bounding boxes
dets = dets[keep, :]
#print "if cls == {0}: {1}".format(str(detectedItem), (cls == detectedItem))
if cls == detectedItem:
print "under else"
inds = np.where(dets[:, -1] >= 0.5)[0] #Threshold applied to score values here
im = im[:, :, (2, 1, 0)]
for i in inds:
print "running for loop"
bbox = dets[i, :4]
detectedBBox = bbox.astype(int)
score = dets[i, -1]
bboxCentroid = c.mathArrayCentroid(detectedBBox)
cv2.circle(im_copy, bboxCentroid, 5, colorScheme, -1) #bbox centroid of detectedItemArray
#Calculate bbox centroid. Use it to determine if the item should be added to detectedItemArray
#Check if the centroid of the detected box is within the designated traffic intersection area
if (str(detectedItem) == "car") or (str(detectedItem) == "bus"):
print "if statement, detected car or bus"
if p.contains_point(bboxCentroid) == 1:
print "within area"
#Calculate corners of interest within the bounding box area and add them all to the corner array
detectedPixels = frame_gray[bbox[1]:bbox[3], bbox[0]:bbox[2]] #[y1:y2, x1:x2]
detectedPixelsColor = im_copy[bbox[1]:bbox[3], bbox[0]:bbox[2]] #for show on colored image
corners = cv2.goodFeaturesToTrack(detectedPixels, mask=detectedPixels, **self.feature_params).reshape(-1, 2)
# for x, y in np.float32(corners).reshape(-1, 2): #black
# cv2.circle(detectedPixels, (x,y), 5, (0, 0, 0), -1)
# cv2.circle(detectedPixelsColor, (x, y), 5, (0, 0, 0), -1)
detectedItemsInThisFrame.append([[detectedBBox, corners]])
else:
print "car/bus not added. Coordinates: ", bbox
else:
print "else not car or bus detected"
detectedPixels = frame_gray[bbox[1]:bbox[3], bbox[0]:bbox[2]] #[y1:y2, x1:x2]
detectedPixelsColor = im_copy[bbox[1]:bbox[3], bbox[0]:bbox[2]] #for show on colored image
corners = cv2.goodFeaturesToTrack(detectedPixels, mask=detectedPixels, **self.feature_params).reshape(-1, 2)
print "detectedItemsInThisFrame len: {0}-------------------------------------".format(len(detectedItemsInThisFrame))
print "detectedItemsInThisFrame: ", detectedItemsInThisFrame
return detectedItemsInThisFrame
def demo_tuples(net, image_name):
"""Detect objects, attributes and relations in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes, attr_scores, rel_scores = im_detect(net, im)
if attr_scores is not None:
print 'Found attribute scores'
if rel_scores is not None:
print 'Found relation scores'
rel_scores = rel_scores[:, 1:] # drop no relation
rel_argmax = np.argmax(rel_scores, axis=1).reshape(
(boxes.shape[0], boxes.shape[0]))
rel_score = np.max(rel_scores, axis=1).reshape(
(boxes.shape[0], boxes.shape[0]))
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.1
NMS_THRESH = 0.05
ATTR_THRESH = 0.1
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im)
# Detections
det_indices = []
det_scores = []
det_objects = []
det_bboxes = []
det_attrs = []
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = np.array(nms(dets, NMS_THRESH))
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= CONF_THRESH)[0]
if len(inds) > 0:
keep = keep[inds]
for k in keep:
det_indices.append(k)
det_bboxes.append(cls_boxes[k])
det_scores.append(cls_scores[k])
det_objects.append(cls)
if attr_scores is not None:
attr_inds = np.where(attr_scores[k][1:] >= ATTR_THRESH)[0]
det_attrs.append([ATTRS[ix] for ix in attr_inds])
else:
det_attrs.append([])
rel_score = rel_score[det_indices].T[det_indices].T
rel_argmax = rel_argmax[det_indices].T[det_indices].T
for i, (idx, score, obj, bbox, attr) in enumerate(
zip(det_indices, det_scores, det_objects, det_bboxes, det_attrs)):
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor='red',
linewidth=3.5))
box_text = '{:s} {:.3f}'.format(obj, score)
if len(attr) > 0:
box_text += "(" + ",".join(attr) + ")"
ax.text(bbox[0],
bbox[1] - 2,
box_text,
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14,
color='white')
# Outgoing
score = np.max(rel_score[i])
ix = np.argmax(rel_score[i])
subject = det_objects[ix]
relation = RELATIONS[rel_argmax[i][ix]]
print 'Relation: %.2f %s -> %s -> %s' % (score, obj, relation, subject)
# Incoming
score = np.max(rel_score.T[i])
ix = np.argmax(rel_score.T[i])
subject = det_objects[ix]
relation = RELATIONS[rel_argmax[ix][i]]
print 'Relation: %.2f %s -> %s -> %s' % (score, subject, relation, obj)
ax.set_title(('detections with '
'p(object|box) >= {:.1f}').format(CONF_THRESH),
fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.draw()
plt.savefig('data/demo/' +
im_file.split('/')[-1].replace(".jpg", "_demo.jpg"))
def proposal_layer(rpn_cls_prob_reshape,
rpn_bbox_pred,
im_info,
cfg_key,
_feat_stride=[
16,
],
anchor_scales=[8, 16, 32],
anchor_ratios=[0.5, 1, 2]):
# Algorithm:
#
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
#layer_params = yaml.load(self.param_str_)
_anchors = generate_anchors(ratios=anchor_ratios,
scales=np.array(anchor_scales))
_num_anchors = _anchors.shape[0]
rpn_cls_prob_reshape = np.transpose(rpn_cls_prob_reshape, [0, 3, 1, 2])
rpn_bbox_pred = np.transpose(rpn_bbox_pred, [0, 3, 1, 2])
#rpn_cls_prob_reshape = np.transpose(np.reshape(rpn_cls_prob_reshape,[1,rpn_cls_prob_reshape.shape[0],rpn_cls_prob_reshape.shape[1],rpn_cls_prob_reshape.shape[2]]),[0,3,2,1])
#rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,2,1])
im_info = im_info[0]
assert rpn_cls_prob_reshape.shape[0] == 1, \
'Only single item batches are supported'
# cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
#cfg_key = 'TEST'
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
min_size = cfg[cfg_key].RPN_MIN_SIZE
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs, which we want
scores = rpn_cls_prob_reshape[:, _num_anchors:, :, :]
bbox_deltas = rpn_bbox_pred
#im_info = bottom[2].data[0, :]
if DEBUG:
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
# 1. Generate proposals from bbox deltas and shifted anchors
height, width = scores.shape[-2:]
if DEBUG:
print('score map size: {}'.format(scores.shape))
# Enumerate all shifts
shift_x = np.arange(0, width) * _feat_stride
shift_y = np.arange(0, height) * _feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors
K = shifts.shape[0]
anchors = _anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size * im_info[2])
proposals = proposals[keep, :]
scores = scores[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)), nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep]
# remove_option = 1
# if ('TEST' == cfg_key and remove_option in [1, 2]):
# # get rid of boxes that are completely inside other boxes
# # with options as to which one to get rid of
# # 1. always the one with lower scores, 2. always the one inside
# new_proposals = []
# removed_indices = set()
# num_props = proposals.shape[0]
# for i in range(num_props):
# if (i in removed_indices):
# continue
# bxA = proposals[i, :]
# for j in range(num_props):
# if ((j == i) or (j in removed_indices)):
# continue
# bxB = proposals[j, :]
# if (bbox_contains(bxA, bxB)):
# if ((1 == remove_option) and (scores[i] != scores[j])):
# if (scores[i] > scores[j]):
# removed_indices.add(j)
# else:
# removed_indices.add(i)
# else: # remove_option == 2 or scores[i] == scores[j]
# removed_indices.add(j)
# nr = len(removed_indices)
# if (nr > 0):
# new_proposals = sorted(set(range(num_props)) - removed_indices)
# proposals = proposals[new_proposals, :]
# scores = scores[new_proposals]
# # padding to make the total number of proposals == post_nms_topN
# proposals = np.vstack((proposals, [proposals[-1, :]] * nr))
# scores = np.vstack((scores, [scores[-1]] * nr))
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
# batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
# BUT we NOW (18-Sep-2017) abuse batch inds, and use it for carrying scores
if ('TEST' == cfg_key):
batch_inds = np.reshape(scores, [proposals.shape[0], 1])
else:
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
if (DEBUG):
print('blob shape: {0}'.format(blob.shape))
print('proposal shape: {0}'.format(proposals.shape))
return blob
def test_net(net, imdb, max_per_image=100, thresh=0.05, vis=False):
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
for i in xrange(num_images):
# filter out any ground truth boxes
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select those the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
if cfg.TEST.BOXSCORE:
boxscores = roidb[i]['boxscores'][roidb[i]['gt_classes'] == 0]
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
if cfg.TEST.BOXSCORE:
scores, boxes = im_detect(net, im, box_proposals, boxscores)
else:
scores, boxes = im_detect(net, im, box_proposals)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in xrange(1, imdb.num_classes):
if scores.shape[1] == 20:
newj = j - 1
else:
newj = j
inds = np.where(scores[:, newj] > thresh)[0]
cls_scores = scores[inds, newj]
cls_boxes = boxes[inds, newj * 4:(newj + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def test_net(net, imdb, max_per_image=100, thresh=0.05, vis=False):
"""Test a Fast R-CNN network on an image database."""
if vis:
from datasets.kitti import kitti
kitti = kitti("valsplit")
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score, [cfg.VIEWP_BINS x viewpoint prob. dist])
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
cache_file = os.path.join(output_dir, 'detections.pkl')
if os.path.exists(cache_file):
with open(cache_file, 'rb') as fid:
all_boxes = cPickle.load(fid)
#print '{} gt roidb loaded from {}'.format(self.name, cache_file)
print 'Detections cache loaded'
warnings.warn("PLEASE MAKE SURE THAT YOU REALLY WANT TO USE THE CACHE!", UserWarning)
#return roidb
else:
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
ndetections = 0
if cfg.SMOOTH_L1_ANGLE:
viewp_bins = 1
elif cfg.CONTINUOUS_ANGLE:
viewp_bins = 1
else:
viewp_bins = cfg.VIEWP_BINS
if cfg.SMOOTH_L1_ANGLE:
allclasses_viewp_bins = imdb.num_classes
elif cfg.CONTINUOUS_ANGLE:
allclasses_viewp_bins = 1
else:
allclasses_viewp_bins = imdb.num_classes*cfg.VIEWP_BINS
for i, img_file in enumerate(imdb.image_index):
if vis:
detts = np.empty([0, 6])
# filter out any ground truth boxes
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select those the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
if cfg.TEST.GTPROPOSALS:
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] > -1]
else:
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
if box_proposals is not None and box_proposals.shape[0] <= 0:
# if there are no proposals....
scores = np.empty((0, imdb.num_classes), dtype=np.float32)
boxes = np.empty((0, imdb.num_classes*4), dtype=np.float32)
if cfg.VIEWPOINTS:
assert cfg.CONTINUOUS_ANGLE==False and cfg.SMOOTH_L1_ANGLE==False, 'not implemented'
viewpoints = np.empty((0, allclasses_viewp_bins), dtype=np.float32)
else:
if cfg.TEST.FOURCHANNELS:
im = cv2.imread(imdb.image_path_at(i), cv2.IMREAD_UNCHANGED)
else:
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
if cfg.VIEWPOINTS:
scores, boxes, viewpoints = im_detect(net, im, box_proposals)
else:
scores, boxes = im_detect(net, im, box_proposals)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
ndetections += len(inds)
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j*4:(j+1)*4]
if cfg.VIEWPOINTS:
if cfg.SMOOTH_L1_ANGLE:
viewp = viewpoints[inds, j]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis], viewp[:, np.newaxis])) \
.astype(np.float32, copy=False)
elif cfg.CONTINUOUS_ANGLE:
viewp = viewpoints[inds]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis], viewp)) \
.astype(np.float32, copy=False)
else:
# Softmax is only performed over the class N_BINSx "slot"
# (that is why we apply it outside Caffe)
cls_viewp = softmax(viewpoints[inds, j*cfg.VIEWP_BINS:(j+1)*cfg.VIEWP_BINS])
# Assert that the result from softmax makes sense
assert(all(abs(np.sum(cls_viewp, axis=1)-1)<0.1))
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis], cls_viewp)) \
.astype(np.float32, copy=False)
else:
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
if cfg.TEST.DO_NMS:
if cfg.USE_CUSTOM_NMS:
if cfg.VIEWPOINTS:
nms_returns = nms(cls_dets[:,:-viewp_bins], cfg.TEST.NMS, force_cpu=True)
else:
nms_returns = nms(cls_dets, cfg.TEST.NMS, force_cpu=True)
if nms_returns:
keep = nms_returns[0]
suppress = nms_returns[1]
else:
keep = []
elif cfg.TEST.SOFT_NMS>0:
if cfg.VIEWPOINTS:
keep = soft_nms(cls_dets[:, :-viewp_bins], method=cfg.TEST.SOFT_NMS)
else:
keep = soft_nms(cls_dets, method=cfg.TEST.SOFT_NMS)
else:
if cfg.VIEWPOINTS:
keep = nms(cls_dets[:,:-viewp_bins], cfg.TEST.NMS)
else:
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
else:
if cfg.VIEWPOINTS:
cls_dets = cls_dets[cls_dets[:,-viewp_bins-1].argsort()[::-1],:]
else:
cls_dets = cls_dets[cls_dets[:,-1].argsort()[::-1],:]
if vis:
pre_detts = np.hstack((np.array(cls_dets[:,:5]), j*np.ones((np.array(cls_dets[:,:5]).shape[0],1))))
detts = np.vstack((detts, pre_detts))
all_boxes[j][i] = cls_dets
if vis:
gt_roidb = kitti._load_kitti_annotation(img_file)
vis_detections(im, imdb.classes, detts, gt_roidb)
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
if cfg.VIEWPOINTS:
image_scores = np.hstack([all_boxes[j][i][:, -viewp_bins-1]
for j in xrange(1, imdb.num_classes)])
else:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
# We usually don't want to do this
print "WARNING! Limiting the number of detections"
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
if cfg.VIEWPOINTS:
keep = np.where(all_boxes[j][i][:, -viewp_bins-1] >= image_thresh)[0]
else:
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print 'im_detect: {:d}/{:d} - {:d} detections - {:.3f}s {:.3f}s' \
.format(i + 1, num_images, ndetections,_t['im_detect'].average_time,
_t['misc'].average_time)
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def test_net(net, imdb, max_per_image=100, thresh=0.05, vis=False):
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
if cfg.TEST.SEG:
n_seg_classes = cfg.SEG_CLASSES
confcounts = np.zeros((n_seg_classes, n_seg_classes))
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
for i in xrange(num_images):
# filter out any ground truth boxes
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select those the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
if cfg.TEST.SEG:
seg_gt = cv2.imread(
get_seg_path(imdb._data_path, imdb.image_path_at(i)), -1)
if seg_gt is None:
print 'Could not read ', get_seg_path(imdb._data_path,
imdb.image_path_at(i))
scores, boxes, seg_scores = im_detect(net, im, box_proposals)
else:
scores, boxes = im_detect(net, im, box_proposals)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack(
[all_boxes[j][i][:, -1] for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
if cfg.TEST.SEG:
# evaluate the segmentation
seg_labels = np.argmax(seg_scores, axis=2).astype(int)
seg_labels = cv2.resize(seg_labels,
(seg_gt.shape[1], seg_gt.shape[0]),
interpolation=cv2.INTER_NEAREST)
sumim = seg_gt + seg_labels * n_seg_classes
hs = np.bincount(sumim.flatten(),
minlength=n_seg_classes * n_seg_classes)
confcounts += hs.reshape((n_seg_classes, n_seg_classes))
print 'Segmentation evaluation'
conf = 100.0 * np.divide(confcounts,
1e-20 + confcounts.sum(axis=1))
np.save(output_dir + '/seg_confusion.npy', conf)
acc = np.zeros(n_seg_classes)
for j in xrange(n_seg_classes):
gtj = sum(confcounts[j, :])
resj = sum(confcounts[:, j])
gtresj = confcounts[j, j]
acc[j] = 100.0 * gtresj / (gtj + resj - gtresj)
print 'Accuracies', acc
print 'Mean accuracy', np.mean(acc)
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def proposal_layer(rpn_cls_prob_reshape,
rpn_bbox_pred,
im_info,
cfg_key,
_feat_stride=[
16,
],
anchor_scales=[8, 16, 32]):
# Algorithm:
#
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
#layer_params = yaml.load(self.param_str_)
_anchors = generate_anchors(scales=np.array(anchor_scales))
_num_anchors = _anchors.shape[0]
rpn_cls_prob_reshape = np.transpose(rpn_cls_prob_reshape, [0, 3, 1, 2])
rpn_bbox_pred = np.transpose(rpn_bbox_pred, [0, 3, 1, 2])
#rpn_cls_prob_reshape = np.transpose(np.reshape(rpn_cls_prob_reshape,[1,rpn_cls_prob_reshape.shape[0],rpn_cls_prob_reshape.shape[1],rpn_cls_prob_reshape.shape[2]]),[0,3,2,1])
#rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,2,1])
im_info = im_info[0]
assert rpn_cls_prob_reshape.shape[0] == 1, \
'Only single item batches are supported'
# cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
#cfg_key = 'TEST'
if type(cfg_key) == bytes:
cfg_key = cfg_key.decode('utf-8')
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
min_size = cfg[cfg_key].RPN_MIN_SIZE
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs, which we want
scores = rpn_cls_prob_reshape[:, _num_anchors:, :, :]
bbox_deltas = rpn_bbox_pred
#im_info = bottom[2].data[0, :]
if DEBUG:
print('im_size: ({}, {})'.format(im_info[0], im_info[1]))
print('scale: {}'.format(im_info[2]))
# 1. Generate proposals from bbox deltas and shifted anchors
height, width = scores.shape[-2:]
if DEBUG:
print('score map size: {}'.format(scores.shape))
# Enumerate all shifts
shift_x = np.arange(0, width) * _feat_stride
shift_y = np.arange(0, height) * _feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors
K = shifts.shape[0]
anchors = _anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size * im_info[2])
proposals = proposals[keep, :]
scores = scores[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)), nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep]
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
return blob
cls_scores = scores[inds, 1]
cls_boxes = boxes[inds, 4:8]
## curve
cls_infos_h = infos_h[inds, :14]
cls_infos_w = infos_w[inds, :14]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
# stack h and w pred.
cls_infos = np.zeros((cls_infos_h.shape[0], 28))
wh_stack_temp = np.dstack((cls_infos_w, cls_infos_h))
assert (wh_stack_temp.shape[0] == cls_infos.shape[0]
), 'wh stack length mismatch.'
for ixstack, row_cls_infos in enumerate(cls_infos):
cls_infos[ixstack] = wh_stack_temp[ixstack].ravel()
cls_dets_withInfo = np.hstack((cls_boxes, cls_scores[:, np.newaxis], cls_infos)) \
.astype(np.float32, copy=False)
cls_dets, cls_dets_withInfo = nps(cls_dets, cls_dets_withInfo)
if cfg.TEST.USE_PNMS:
keep = pnms(cls_dets_withInfo, cfg.TEST.PNMS)
else:
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
cls_dets_withInfo = cls_dets_withInfo[keep, :]
vis(im, cls_dets_withInfo, 0.1)
def detect(self, im, im3d, detection_graph, trackingSess, adrfpFrame):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
#im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
#im_file = os.path.join('/home/corgi/Lab/label/pos_frame/ACCV/training/000001/',image_name)
#im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(self.sess, self.net, im)
hboxes, hscores = detector_utils.detect_objects(
im, detection_graph, trackingSess)
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
num_hands_detect = 1
score_thresh = 0.5
points = detector_utils.draw_box_on_image(num_hands_detect,
score_thresh, hscores,
hboxes, 320, 240, im,
adrfpFrame)
# Visualize detections for each class
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
if cls == "hand": continue
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= 0.7)[0]
if len(inds) == 0:
#print "preskacem "+str(dets[:, -1])
continue
im = self.vis_detections(im, cls, dets, inds, im3d, adrfpFrame,
points)
img = im[:, :, :].copy()
img[:, :, 2] = im[:, :, 0].copy()
img[:, :, 0] = im[:, :, 2].copy()
name = None
if self.iterPic < 10:
name = "0" + str(self.iterPic)
else:
name = str(self.iterPic)
cv2.imwrite(name + '.png', img)
self.iterPic += 1
return im
def test_net(net, imdb, max_per_image=400, thresh=-np.inf, vis=False):
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
# imdb=get_testing_roidb(imdb)
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
# width = imdb._get_widths()
for i in xrange(num_images):
# filter out any ground truth boxes
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select those the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
im = cv2.imread(imdb.image_path_at(i))
width = im.shape[1]
_t['im_detect'].tic()
scores, boxes = im_detect(net, im, box_proposals)
if cfg.TEST.USE_FLIPPED:
# image flipped
flipped_im= cv2.flip(im,1)
flip_scores,flip_boxes=im_detect(net, flipped_im, box_proposals)
for k in xrange(flip_boxes.shape[1]/4):
# if True:
# vis_detections(flipped_im, imdb.classes[k], flip_boxes[:,k*4:k*4+4])
# # raw_input()
oldx1 = flip_boxes[:, k*4].copy()
oldx2 = flip_boxes[:, k*4+2].copy()
assert (flip_boxes[:, k*4] >= 0).all()
assert (flip_boxes[:, k*4+2] >= flip_boxes[:, k*4]).all()
assert (width>= flip_boxes[:, k*4+2]).all()
flip_boxes[:, k*4] = width - oldx2
flip_boxes[:,k*4+2] = width - oldx1
assert(flip_boxes[:, k*4+2]>=0).all()
assert(flip_boxes[:,k*4]>=0).all()
assert (width>= flip_boxes[:, k*4+2]).all()
assert (flip_boxes[:, k*4+2] >= flip_boxes[:, k*4]).all()
boxes = np.concatenate( (boxes, flip_boxes.copy()), axis = 0 )
scores = np.concatenate( (scores, flip_scores.copy()), axis = 0 )
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
if cfg.TEST.AGNOSTIC:
cls_boxes = boxes[inds, 4:8]
else:
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def demo(net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
img_name = os.path.basename(image_name)
# im_file = image_name
# im = cv2.imread(im_file)
im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
im = cv2.imread(im_file)
pimg = process_image(im)
# cv2.imshow("Processed", pimg)
# cv2.waitKey(0)
im = pimg
height, width = im.shape[:2]
mid = width / 2.5
# print('height = {} and width/2.5 = {}'.format(height, mid))
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
# print ('Detection took {:.3f}s for '
# '{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
# vis_detections(im, cls, dets, thresh=CONF_THRESH)
font = cv2.FONT_HERSHEY_SIMPLEX
# print 'class index is {}'.format(cls_ind)
color = (0, 0, 255)
inds = np.where(dets[:, -1] >= CONF_THRESH)[0]
if len(inds) > 0:
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
color, 2)
if bbox[0] < mid:
cv2.putText(im, 'left {:s}'.format(cls), (bbox[0], (int)(
(bbox[1] - 2))), font, 0.5, (255, 0, 0), 1)
else:
cv2.putText(im, 'right {:s}'.format(cls, score),
(bbox[0], (int)(
(bbox[1] - 2))), font, 0.5, (255, 0, 0), 1)
# cv2.putText(im,'{:s} {:.3f}'.format(cls, score),(bbox[0], (int)((bbox[1]- 2))), font, 0.5, (255,255,255), 1)
# Write the resulting frame
# print 'Final image name is {}'.format(img_name)
splitName = os.path.splitext(img_name)[0]
# print (os.path.splitext(img_name)[0])
# print splitName
# cv2.imwrite('{:s}_output.jpg'.format(splitName), im)
## Display output frame
# cv2.imshow("output", im)
# cv2.waitKey(0)
## Write output frame
opDir = '/home/student/cmpe295-masters-project/faster-rcnn-resnet/data/output/'
cv2.imwrite(os.path.join(opDir, img_name), im)
def test_net(net, imdb,cfg,max_per_image=100, thresh=0.05, vis=False,task = 'det',nmhead=''):
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# heuristic: keep an average of 40 detections per class per images prior
# to NMS
max_per_set = 40 * num_images
# heuristic: keep at most 100 detection per class per image prior to NMS
max_per_image = 100
# detection threshold for each class (this is adaptively set based on the
# max_per_set constraint)
thresh = -np.inf * np.ones(imdb.num_classes)
# top_scores will hold one minheap of scores per class (used to enforce
# the max_per_set constraint)
top_scores = [[] for _ in xrange(imdb.num_classes)]
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
#print output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
#if not os.path.exists(output_dir):
# os.makedirs(output_dir)
do_det = task=='det';
det_file = os.path.join(output_dir, 'detections.pkl')
if (do_det and os.path.isfile(det_file)):
all_boxes = cPickle.load(open(det_file,'r'));
else:
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
if not cfg.TRAIN['HAS_RPN']:
roidb = imdb.roidb
#only_seg = cfg.TRAIN['USE_SEG'] & (not cfg.TRAIN['USE_DET'])
do_seg = task=='seg'
do_edg = task=='edg'
do_nrm = task=='nrm'
do_sbd = task=='sbd'
do_prt = task=='prt'
do_sal = task=='sal'
print "num_images = ",num_images
for i in xrange(num_images):
next_file = os.path.join(output_dir,imdb.image_index[i] + '.mat')
next_file_png = os.path.join(output_dir,imdb.image_index[i] + '.png')
if os.path.exists(next_file) or os.path.exists(next_file_png):
continue
im = cv2.imread(imdb.image_path_at(i))
#shape = im.shape
#print im.shape
_t['im_detect'].tic()
scores, boxes = im_detect(net, im,cfg, None,do_det)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
if do_det:
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > 0.05)[0]
cls_scores = scores[inds, j]
if cfg.TEST.AGNOSTIC:
cls_boxes = boxes[inds, 4:8]
else:
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
#print "here!!"
if do_edg:
if nmhead[0:4]!='sigm':
nmhead = 'sigmoid-'+nmhead
#from IPython import embed; embed()
if (do_seg or do_prt):
data = net.blobs[nmhead].data.copy();
data= data.squeeze(axis=0)
posteriors = data.argmax(axis=0)
posteriors = posteriors.astype('uint8').copy();
from PIL import Image
im = Image.fromarray(posteriors)
im.save(next_file_png)
if (do_sal or do_edg):
#print "nmhead: ",nmhead
data = net.blobs[nmhead].data.copy();
if nmhead=='mtn_result1':
slice = 0;
else:
slice = 1;
data= data.squeeze(axis=0)
posteriors = 255.0*visualize_gray(data,slice);
posteriors = posteriors.astype('uint8').copy();
from PIL import Image
im = Image.fromarray(posteriors)
im.save(next_file_png)
if (do_nrm):
data =net.blobs[nmhead].data.copy();
data = -128.*data.copy().squeeze(axis=0) + 128.
data = np.transpose(data,(1,2,0))
matplotlib.image.imsave(next_file_png,data.astype('uint8').copy())
if do_sbd:
d = {'res':net.blobs[nmhead].data.copy()};
scipy.io.savemat(next_file,d, oned_as='column')
# end of for-loop over images
if do_det:
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
if do_det:
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def test_net_mask_reload(net_proto,
net_mask_proto,
weights,
imdb,
max_per_image=400,
thresh=-np.inf,
vis=False,
save_path="./output/"):
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
caffe.set_mode_gpu()
caffe.set_device(cfg.GPU_ID)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
net = caffe.Net(net_proto, weights, caffe.TEST)
net.name = os.path.splitext(os.path.basename(weights))[0]
output_dir = get_output_dir(imdb, net)
if not os.path.exists(save_path):
os.makedirs(save_path)
# timers
_t = {'im_detect': Timer(), 'im_seg': Timer(), 'misc': Timer()}
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
del net
for i in xrange(num_images):
# filter out any ground truth boxes
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select those the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
net = caffe.Net(net_proto, weights, caffe.TEST)
net.name = os.path.splitext(os.path.basename(weights))[0]
im = cv2.imread(imdb.image_path_at(i))
print(im.shape)
_t['im_detect'].tic()
scores, boxes, feat = im_det(net, im, box_proposals)
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
if cfg.TEST.AGNOSTIC:
cls_boxes = boxes[inds, 4:8]
else:
cls_boxes = boxes[inds, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
cls_dets = cls_dets[keep, :]
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack(
[all_boxes[j][i][:, -1] for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
print 'im_detection: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
del net
net_mask = caffe.Net(net_mask_proto, weights, caffe.TEST)
net_mask.name = os.path.splitext(os.path.basename(weights))[0]
_t['im_seg'].tic()
out_mask = np.zeros((im.shape[0], im.shape[1]))
for j in xrange(1, imdb.num_classes):
ins_index = 1
boxes_this_im = all_boxes[j][i][:, :-1]
seg = im_seg(net_mask, im, feat, boxes_this_im)
# print(seg.shape)
for ii in xrange(seg.shape[0]):
seg_now = seg[ii][0]
# seg_now = np.transpose(seg_now)
box_now = boxes_this_im[ii]
box_now = box_now.astype(int)
if box_now[2] == box_now[0] or box_now[3] == box_now[1]:
continue
# im_now=im[box_now[1]: box_now[3], box_now[0]:box_now[2]]
# cv2.imshow("test", im_now)
# cv2.waitKey()
seg_org_size = cv2.resize(
seg_now,
(box_now[2] - box_now[0], box_now[3] - box_now[1]),
interpolation=cv2.INTER_NEAREST)
# print((seg_org_size*99999).shape)
# cv2.imshow("seg", seg_org_size*99999)
# cv2.waitKey()
seg_org_size = seg_org_size * ins_index
out_mask[box_now[1]:box_now[3],
box_now[0]:box_now[2]] = seg_org_size
ins_index += 1
_t['im_seg'].toc()
mask_save_path = os.path.join(
save_path,
os.path.basename(imdb.image_path_at(i)).replace(".jpg", ".png"))
cv2.imwrite(mask_save_path, out_mask * 10)
print 'im_seg: {:d}/{:d} {:.3f}s' \
.format(i + 1, num_images, _t['im_seg'].average_time)
del net_mask
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def demo_tuples(net, image_name):
"""Detect objects, attributes and relations in an image using pre-computed object proposals."""
image_num = int(image_name.split(".")[0])
att_unique = np.unique(att_names[image_num * scale:(image_num * scale +
scale)])
print(att_unique)
att_unique_adv = np.unique(
att_names_adv[image_num * scale:(image_num * scale + scale)])
cls_unique = np.unique(att_cls[image_num * scale:(image_num * scale +
scale)])
print(cls_unique)
cls_unique_adv = np.unique(att_cls_adv[image_num *
scale:(image_num * scale + scale)])
# Load the demo image
im_file = os.path.join(
"/media/sadaf/e4da0f25-29be-4c9e-a432-3193ff5f5baf/Code/Pytorch_Code/transfer_learn/Analysis/CUB_clean",
image_name)
im = cv2.imread(im_file)
print(im.shape)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes, attr_scores, rel_scores = im_detect(net, im)
if attr_scores is not None:
print 'Found attribute scores'
"""
if rel_scores is not None:
print 'Found relation scores'
rel_scores = rel_scores[:,1:] # drop no relation
rel_argmax = np.argmax(rel_scores, axis=1).reshape((boxes.shape[0],boxes.shape[0]))
rel_score = np.max(rel_scores, axis=1).reshape((boxes.shape[0],boxes.shape[0]))
"""
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.2
NMS_THRESH = 0.05
ATTR_THRESH = 0.1
im = im[:, :, (2, 1, 0)]
#im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
fig, ax = plt.subplots(figsize=(12, 12))
plt.imshow(im)
# Detections
det_indices = []
det_scores = []
det_objects = []
det_bboxes = []
det_attrs = []
for cls_ind, cls in enumerate(classes[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = np.array(nms(dets, NMS_THRESH))
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= CONF_THRESH)[0]
if len(inds) > 0:
keep = keep[inds]
for k in keep:
det_indices.append(k)
det_bboxes.append(cls_boxes[k])
det_scores.append(cls_scores[k])
det_objects.append(cls)
if attr_scores is not None:
attr_inds = np.where(attr_scores[k][1:] >= ATTR_THRESH)[0]
det_attrs.append([attributes[ix] for ix in attr_inds])
else:
det_attrs.append([])
#rel_score = rel_score[det_indices].T[det_indices].T
#rel_argmax = rel_argmax[det_indices].T[det_indices].T
for i, (idx, score, obj, bbox, attr) in enumerate(
zip(det_indices, det_scores, det_objects, det_bboxes, det_attrs)):
attr_s = [element for element in attr if element in att_unique]
for i in range(len(attr)):
if attr[i] in att_unique:
if obj in cls_unique:
box_text = obj
if len(attr) > 0:
box_text + " " + (attr[i])
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor='red',
linewidth=2,
alpha=0.5))
ax.text(bbox[0],
bbox[1] - 2,
'%s' % (box_text),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=10,
color='white')
plt.axis('off')
plt.tight_layout()
plt.draw()
plt.savefig(
'/media/sadaf/e4da0f25-29be-4c9e-a432-3193ff5f5baf/Code/Pytorch_Code/transfer_learn/Analysis/clean_bb/'
+ image_name)
def demoVideo(image):
global count
global cls_label
global b_box
count = count + 1
# print ('count before = {}'.format(count))
if (count % 10) > 0:
im = process_image(image)
height, width = im.shape[:2]
mid = width / 2.5
if cls_label is not None:
# print('saved label is = {}'.format(cls_label))
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.rectangle(im, (b_box[0], b_box[1]), (b_box[2], b_box[3]),
(0, 0, 255), 2)
if b_box[0] < mid:
# cv2.putText(im,'left {:s}'.format(label),(b_box[0], (int)((b_box[1]- 2))), cv2.FONT_HERSHEY_PLAIN, fontScale=1.25, thickness=3, color=(255, 255, 255))
cv2.putText(im, 'left {:s}'.format(cls_label),
(b_box[0], (int)(
(b_box[1] - 2))), font, 0.5, (255, 0, 0), 1)
else:
cv2.putText(im, 'right {:s}'.format(cls_label),
(b_box[0], (int)(
(b_box[1] - 2))), font, 0.5, (255, 0, 0), 1)
return im
im = process_image(image)
height, width = im.shape[:2]
mid = width / 2.5
# print('height = {} and width/2.5 = {}'.format(height, mid))
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(default_net, im)
timer.toc()
# print ('Detection took {:.3f}s for '
# '{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
cls_label = None
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
font = cv2.FONT_HERSHEY_SIMPLEX
color = (0, 0, 255)
inds = np.where(dets[:, -1] >= CONF_THRESH)[0]
if len(inds) > 0:
for i in inds:
bbox = dets[i, :4]
b_box = bbox
score = dets[i, -1]
cls_label = cls
cv2.rectangle(im, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
color, 2)
if bbox[0] < mid:
cv2.putText(im, 'left {:s}'.format(cls), (bbox[0], (int)(
(bbox[1] - 2))), font, 0.5, (255, 0, 0), 1)
else:
cv2.putText(im, 'right {:s}'.format(cls, score),
(bbox[0], (int)(
(bbox[1] - 2))), font, 0.5, (255, 0, 0), 1)
# cv2.putText(im,'{:s} {:.3f}'.format(cls, score),(bbox[0], (int)((bbox[1]- 2))), font, 0.5, (255,255,255), 1)
return im
def forward(self, bottom, top):
# Algorithm:
#
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
assert bottom[0].data.shape[0] == 1, \
'Only single item batches are supported'
cfg_key = str('TRAIN' if self.phase == 0 else 'TEST') # either 'TRAIN' or 'TEST'
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
min_size = cfg[cfg_key].RPN_MIN_SIZE
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs, which we want
scores = bottom[0].data[:, self._num_anchors:, :, :]
bbox_deltas = bottom[1].data
im_info = bottom[2].data[0, :]
if DEBUG:
print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
print 'scale: {}'.format(im_info[2])
# 1. Generate proposals from bbox deltas and shifted anchors
height, width = scores.shape[-2:]
if DEBUG:
print 'score map size: {}'.format(scores.shape)
# Enumerate all shifts
shift_x = np.arange(0, width) * self._feat_stride
shift_y = np.arange(0, height) * self._feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
shift_x.ravel(), shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = self._num_anchors
K = shifts.shape[0]
anchors = self._anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape((-1, 4))
if cfg_key == 'TRAIN' and cfg.TRAIN.RPN_NORMALIZE_TARGETS:
bbox_deltas *= cfg.TRAIN.RPN_NORMALIZE_STDS
bbox_deltas += cfg.TRAIN.RPN_NORMALIZE_MEANS
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = scores.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size * im_info[2])
proposals = proposals[keep, :]
scores = scores[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)), nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores = scores[keep]
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
# print blob.shape
top[0].reshape(*(blob.shape))
top[0].data[...] = blob
if DEBUG_SHAPE:
print 'ProposalLayer top[0] size: {}'.format(top[0].data.shape)
# [Optional] output scores blob
if len(top) > 1:
top[1].reshape(*(scores.shape))
top[1].data[...] = scores
if DEBUG_SHAPE:
print 'ProposalLayer top[0] size: {}'.format(top[0].data.shape)
def demo(net, image_list):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.ROOT_DIR, 'data', image_list[0])
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
ind = 1
color_list = [(255, 0, 0), (0, 255, 0), (0, 0, 255)]
color_cls = [(0, 255, 255), (255, 0, 255), (255, 255, 0)]
for j in range(1, len(CLASSES)):
num_objs = int(image_list[ind + 1])
for i in xrange(num_objs):
x1 = int(float(image_list[ind + 2 + i * 4]))
y1 = int(float(image_list[ind + 3 + i * 4]))
x2 = int(float(image_list[ind + 4 + i * 4]))
y2 = int(float(image_list[ind + 5 + i * 4]))
rect_start = (x1, y1)
rect_end = (x2, y2)
#cv2.rectangle(im, rect_start, rect_end, color_list[j-1], 2)
ind += 4 * num_objs + 1
thresh = 0.5
NMS_THRESH = 0.3
path = os.path.join(cfg.ROOT_DIR, 'data', 'results', 'show',
image_list[0][17:])
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= thresh)[0]
index = 1
if len(inds) == 0 and index == len(CLASSES[1:]):
cv2.imwrite(path, im)
return
elif len(inds) == 0 and index < len(CLASSES[1:]):
index += 1
continue
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
x = bbox[0]
y = bbox[1]
rect_start = (x, y)
x1 = bbox[2]
y1 = bbox[3]
rect_end = (x1, y1)
color_pred = color_cls[cls_ind - 1]
cv2.rectangle(im, rect_start, rect_end, color_pred, 2)
cv2.imwrite(path, im)
def test_net(net, imdb):
"""Test a Fast R-CNN network on an image database."""
num_images = len(imdb.image_index)
# heuristic: keep an average of 40 detections per class per images prior
# to NMS
max_per_set = 40 * num_images
# heuristic: keep at most 100 detection per class per image prior to NMS
max_per_image = 100
# detection thresold for each class (this is adaptively set based on the
# max_per_set constraint)
thresh = -np.inf * np.ones(imdb.num_classes)
# top_scores will hold one minheap of scores per class (used to enforce
# the max_per_set constraint)
top_scores = [[] for _ in xrange(imdb.num_classes)]
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
for i in xrange(num_images):
# filter out any ground truth boxes
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
scores, boxes = im_detect(net, im, box_proposals)
_t['im_detect'].toc()
_t['misc'].tic()
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh[j])[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j * 4:(j + 1) * 4]
top_inds = np.argsort(-cls_scores)[:max_per_image]
cls_scores = cls_scores[top_inds]
cls_boxes = cls_boxes[top_inds, :]
# push new scores onto the minheap
for val in cls_scores:
heapq.heappush(top_scores[j], val)
# if we've collected more than the max number of detection,
# then pop items off the minheap and update the class threshold
if len(top_scores[j]) > max_per_set:
while len(top_scores[j]) > max_per_set:
heapq.heappop(top_scores[j])
thresh[j] = top_scores[j][0]
all_boxes[j][i] = \
np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
if 0:
keep = nms(all_boxes[j][i], 0.3)
vis_detections(im, imdb.classes[j], all_boxes[j][i][keep, :])
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
for j in xrange(1, imdb.num_classes):
for i in xrange(num_images):
inds = np.where(all_boxes[j][i][:, -1] > thresh[j])[0]
all_boxes[j][i] = all_boxes[j][i][inds, :]
det_file = os.path.join(output_dir, 'detections.pkl')
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Applying NMS to all detections'
nms_dets = apply_nms(all_boxes, cfg.TEST.NMS)
print 'Evaluating detections'
imdb.evaluate_detections(nms_dets, output_dir)
def get_detections_from_im(net, im_file, image_id, conf_thresh=0.2, visualize=False):
"""Load im_file and extract bottom-up features using Faster RCNN"""
MIN_BOXES, MAX_BOXES=36,36
NMS_THRESH = 0.05
CONF_THRESH = 0.1
ATTR_THRESH = 0.1
im = cv2.imread(im_file)
scores, boxes, attr_scores, rel_scores = im_detect(net, im)
# Keep the original boxes, don't worry about the regresssion bbox outputs
rois = net.blobs['rois'].data.copy()
# unscale back to raw image space
blobs, im_scales = _get_blobs(im, None)
cls_boxes = rois[:, 1:5] / im_scales[0]
cls_prob = net.blobs['cls_prob'].data
pool5 = net.blobs['pool5_flat'].data
# Keep only the best detections
max_conf = np.zeros((rois.shape[0]))
for cls_ind in range(1,cls_prob.shape[1]):
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = np.array(nms(dets, cfg.TEST.NMS))
max_conf[keep] = np.where(cls_scores[keep] > max_conf[keep], cls_scores[keep], max_conf[keep])
keep_boxes = np.where(max_conf >= conf_thresh)[0]
if len(keep_boxes) < MIN_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MIN_BOXES]
elif len(keep_boxes) > MAX_BOXES:
keep_boxes = np.argsort(max_conf)[::-1][:MAX_BOXES]
#Normalize scores of best detections, sum their features to
#obtain bottom-up attention features of im
best_scores = max_conf[keep_boxes]
best_feats = pool5[keep_boxes]
scores_norm = np.expand_dims(np.exp(best_scores)/np.sum(np.exp(best_scores))+eps,axis=1)
cumulative_feats = scores_norm.T.dot(best_feats)
sum_feats = np.sum(best_feats,axis=0)
if visualize:
#To visualize the top scoring bounding boxes overlaid on the image im
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
if attr_scores is not None:
attributes = attr_scores[keep]
else:
attributes = None
if rel_scores is not None:
rel_argmax_c = rel_argmax[keep]
rel_score_c = rel_score[keep]
else:
rel_argmax_c = None
rel_score_c = None
vis_detections(ax, cls, dets, attributes, rel_argmax_c, rel_score_c, thresh=CONF_THRESH)
plt.savefig('./'+im_file.split('/')[-1].replace(".jpg", "_demo.png"))
return {
'image_id': image_id,
'image_h': np.size(im, 0),
'image_w': np.size(im, 1),
'num_boxes' : len(keep_boxes),
'boxes': base64.b64encode(cls_boxes[keep_boxes]),
'features': base64.b64encode(pool5[keep_boxes]),
'cumulative_feats':cumulative_feats, #softmax normalized features of best roi's
'sum_feats':sum_feats # features of best roi's summed
}
def test_net(net, imdb, max_per_image=100, thresh=0.05, boxes_num_per_batch=0, vis=False, startIdx=0, endIdx=-1, saveMat=False, svm=False, use_wzctx=True):
"""Test a Fast R-CNN network on an image database."""
if use_wzctx:
print "use use_wzctx!!!"
num_images = len(imdb.image_index)
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
#print "4"
all_boxes = [[[] for _ in xrange(num_images)]
for _ in xrange(imdb.num_classes)]
output_dir = get_output_dir(imdb, net)
# timers
_t = {'im_detect' : Timer(), 'misc' : Timer()}
#print "5"
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
if endIdx==-1:
endIdx=num_images
#print "6"
for i in xrange(num_images):
# filter out any ground truth boxes
if i < startIdx or i>=endIdx:
continue
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select those the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
#print "x"
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
#print "y"
im_name = imdb.image_path_at(i)
im_name = im_name.split('/')[-1]
im_name = im_name.split('.')[0]
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
#print "boxes_num %d"%boxes_num_per_batch
if boxes_num_per_batch > 0:
num_boxes = box_proposals.shape[0]
num_batch = (num_boxes + boxes_num_per_batch -1) / boxes_num_per_batch
#print "zzz"
#num_boxes = roidb[i]['boxes'].shape[0]
#num_batch = math.ceil(num_boxes/boxes_num_per_batch)
scores_batch = np.zeros((num_batch*boxes_num_per_batch, imdb.num_classes), dtype=np.float32)
boxes_batch = np.zeros((num_batch*boxes_num_per_batch, 4*imdb.num_classes), dtype=np.float32)
# replicate the first box num_batch*boxes_num_per_batch times for preallocation
rois = np.tile(box_proposals[0, :], (num_batch*boxes_num_per_batch, 1))
#print "xx"
# assign real boxes to rois
rois[:num_boxes, :] = box_proposals
#print "num_batch: %d"%num_batch
for j in xrange(int(num_batch)):
roi = rois[j*boxes_num_per_batch:(j+1)*boxes_num_per_batch, :]
#print roi.shape
score, box = im_detect(net, im, roi, svm, use_wzctx)
scores_batch[j*boxes_num_per_batch:(j+1)*boxes_num_per_batch, :] = score# [:,:,0,0]
boxes_batch[j*boxes_num_per_batch:(j+1)*boxes_num_per_batch, :] = box
# print "6_%d"%j
# discard duplicated results
scores = scores_batch[:num_boxes, :]
#print "kx"
boxes = boxes_batch[:num_boxes, :]
else:
#print box_proposals.shape[0]
scores, boxes = im_detect(net, im, box_proposals, svm, use_wzctx)
mat_dir = os.path.join(output_dir, 'stage%s'%startIdx)
if not os.path.exists(mat_dir):
os.mkdir(mat_dir)
if True:
sio.savemat('%s/%s.mat' % (mat_dir,im_name + '_' + str(i) ), {'scores': scores, 'boxes': boxes})
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
#print "7"
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j*4:(j+1)*4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS)
if cfg.TEST.BBOX_VOTE:
cls_dets_after_nms = cls_dets[keep, :]
cls_dets = bbox_voting(cls_dets_after_nms, cls_dets, threshold=cfg.TEST.BBOX_VOTE_THRESH)
else:
cls_dets = cls_dets[keep, :]
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack([all_boxes[j][i][:, -1]
for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
#det_file = os.path.join(output_dir, 'detection_%sto%s.pkl' % (startIdx,endIdx))
#with open(det_file, 'wb') as f:
# cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL)
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir, startIdx, endIdx)
numberImg += 1
im = cv2.imread("/nfs/zhengmeisong/wkspace/caffe_wk/py-faster-rcnn/data/test/"+filename)
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print ('No.{:d} took {:.3f}s for '
'{:d} object proposals').format(numberImg, timer.total_time, boxes.shape[0])
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)] #300*4矩阵
cls_scores = scores[:, cls_ind] #300行
dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
inds = np.where(dets[:, -1] >= CONF_THRESH)[0]
if len(inds) == 0:
continue
print inds,dets[inds[0]]
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
cv2.rectangle(im, (bbox[0], bbox[1]),
(bbox[2],bbox[3]),
(0,0,255),2)
cv2.putText(im, '{:s} {:.3f}'.format(cls, score),(bbox[0], bbox[1]),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255),1,4,0)
cv2.imwrite("/nfs/zhengmeisong/wkspace/caffe_wk/py-faster-rcnn/data/testOut/"+filename, im);
def nms_detections(pred_boxes, scores, nms_thresh, inds=None):
dets = np.hstack((pred_boxes, scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, nms_thresh)
if inds is None:
return pred_boxes[keep], scores[keep], keep
return pred_boxes[keep], scores[keep], inds[keep], keep
def vis_detections(im, wav_name, scores, boxes):
"""Draw detected bounding boxes."""
'''
im = im[:, :, (2, 1, 0)]
im_size=im.shape
fig, ax = plt.subplots(figsize=(im_size[1]/100.0, im_size[0]/100.0))
ax.imshow(im, aspect='equal')
'''
result = open(data_folder + 'Result/task2_results.txt', 'at')
f = open(
data_folder + 'Result/estimate_txt/' + wav_name[:-4] + '_estimate.txt',
'wt')
result.write(wav_name)
write_result = False
cls_boxes = np.zeros((boxes.shape[0], 4), boxes.dtype)
cls_boxes[:, 1] = boxes[:, 2]
cls_boxes[:, 2] = 511
cls_boxes[:, 3] = boxes[:, 3]
for cls_ind, class_name in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
#np.save('data/'+im_name+'_keep.npy',keep)
#sio.savemat('data/'+im_name+'_keep.mat',{'keep':keep})
#dets = dets[keep, :]
#dets=dets[np.argmax(dets[:,-1]),:]
#dets=dets[np.newaxis,:]
'''
for i in keep:
if dets[i,-1]>(CONF_THRESH[cls_ind]+0.5*(1-scores[i,0])):
'''
dets = dets[keep, :]
inds = np.where(dets[:, -1] > CONF_THRESH[cls_ind])[0]
if len(inds) == 0:
continue
else:
for i in inds:
onset = str(dets[i, 1] * (nfft - noverlap) / common_fs)
offset = str(dets[i, 3] * (nfft - noverlap) / common_fs)
if not write_result:
result.write('\t' + onset + '\t' + offset + '\t' +
name_transform[class_name])
write_result = True
else:
result.write('\n' + wav_name + '\t' + onset + '\t' +
offset + '\t' + name_transform[class_name])
f.write(onset + '\t' + offset + '\t' +
name_transform[class_name] + '\n')
'''
ax.add_patch(
plt.Rectangle((dets[i,0], dets[i,1]),
dets[i,2] - dets[i,0],
dets[i,3] - dets[i,1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(dets[i,0], dets[i,1] - 2,
'{:s} {:.3f}'.format(class_name, dets[i,-1]),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
'''
result.write('\n')
result.close()
f.close()
'''
def demo(net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, image_name)
im = cv2.imread(im_file)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes, attr_scores, rel_scores = im_detect(net, im)
#print 'relations'
#print rel_scores.shape
#rel_argmax = np.argsort(rel_scores, axis=1).reshape((boxes.shape[0],boxes.shape[0]))
#rel_score = np.max(rel_scores, axis=1).reshape((boxes.shape[0],boxes.shape[0]))
#print rel_argmax.shape
#print rel_score.shape
#print np.min(rel_score)
#print np.max(rel_score)
#np.savetxt('rel_score.csv', rel_score, delimiter=',')
#np.savetxt('rel_argmax.csv', rel_argmax, delimiter=',')
#print fail
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
CONF_THRESH = 0.4
NMS_THRESH = 0.3
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
if attr_scores is not None:
attributes = attr_scores[keep]
else:
attributes = None
if rel_scores is not None:
rel_argmax_c = rel_argmax[keep]
rel_score_c = rel_score[keep]
else:
rel_argmax_c = None
rel_score_c = None
vis_detections(ax,
cls,
dets,
attributes,
rel_argmax_c,
rel_score_c,
thresh=CONF_THRESH)
plt.savefig('data/demo/' +
im_file.split('/')[-1].replace(".jpg", "_demo.jpg"))
def forward(self, bottom, top):
# params
cfg_key = self.phase # either 'TRAIN' or 'TEST'
if cfg_key == 0:
cfg_ = cfg.TRAIN
else:
cfg_ = cfg.TEST
# corner params
pt_thres = cfg_.PT_THRESH
pt_max_num = cfg.PT_MAX_NUM
pt_nms_range = cfg.PT_NMS_RANGE
pt_nms_thres = cfg.PT_NMS_THRESH
# proposal params
ld_interval = cfg.LD_INTERVAL
ld_um_thres = cfg.LD_UM_THRESH
# rpn params
# min_size = cfg_.RPN_MIN_SIZE
nms_thresh = cfg_.RPN_NMS_THRESH
pre_nms_topN = cfg_.RPN_PRE_NMS_TOP_N
post_nms_topN = cfg_.RPN_POST_NMS_TOP_N
im_info = bottom[0].data[0, :]
score_tl = bottom[1].data[0, :].transpose((1, 2, 0))
score_tr = bottom[2].data[0, :].transpose((1, 2, 0))
score_br = bottom[3].data[0, :].transpose((1, 2, 0))
score_bl = bottom[4].data[0, :].transpose((1, 2, 0))
scores = np.concatenate([
score_tl[:, :, :, np.newaxis], score_tr[:, :, :, np.newaxis],
score_br[:, :, :, np.newaxis], score_bl[:, :, :, np.newaxis]
],
axis=3)
map_info = scores.shape[:2]
# 1. sample corner candidates from prob maps
tl, tr, br, bl = _corner_sampling(scores, pt_thres, pt_max_num,
pt_nms_range, pt_nms_thres)
# 2. assemble corner candidates into proposals
proposals = _proposal_sampling(tl, tr, br, bl, map_info, ld_interval,
ld_um_thres)
# 3. filter
proposals = filter_quads(proposals)
scores = proposals[:, 8]
proposals = proposals[:, :8]
# 3. rescale quads into raw image space
proposals = proposals * self._feat_stride
# 4. quadrilateral non-max surpression
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores = scores[order]
keep = nms(
np.hstack((proposals, scores[:, np.newaxis])).astype(np.float32,
copy=False),
nms_thresh)
proposals = proposals[keep, :]
scores = scores[keep]
if post_nms_topN > 0:
proposals = proposals[:post_nms_topN, :]
scores = scores[:post_nms_topN]
if proposals.shape[0] == 0:
# add whole image to avoid error
print 'NO PROPOSALS!'
proposals = np.array(
[[0, 0, im_info[1], 0, im_info[1], im_info[0], 0, im_info[0]]])
scores = np.array([0.0])
# output
# top[0]: quads(x1, y1, x2, y2, x3, y3, x4, y4)
# top[1]: rois(xmin, ymin, xmax, ymax, theta)
# top[2]: scores
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, proposals.astype(np.float32,
copy=False)))
top[0].reshape(*blob.shape)
top[0].data[...] = blob
if len(top) > 1:
if cfg.DUAL_ROI:
rois = quad_2_obb(np.array(proposals, dtype=np.float32))
rois = dual_roi(rois)
else:
rois = quad_2_obb(np.array(proposals, dtype=np.float32))
batch_inds = np.zeros((rois.shape[0], 1), dtype=np.float32)
blob = np.hstack((batch_inds, rois.astype(np.float32, copy=False)))
top[1].reshape(*blob.shape)
top[1].data[...] = blob
if len(top) > 2:
scores = np.vstack((scores, scores)).transpose()
top[2].reshape(*scores.shape)
top[2].data[...] = scores
def test_net(net, imdb, max_per_image=100, thresh=0.05, vis=False):
"""Test a Fast R-CNN network on an image database."""
num_images = len(
imdb.image_index) # 这是 imdb 子类(比如 pascal_voc.py)从txt里读取的所有测试图片名称
# all detections are collected into:
# all_boxes[cls][image] = N x 5 array of detections in
# (x1, y1, x2, y2, score)
all_boxes = [
[[] for _ in xrange(num_images)] # 结构：[num_classes, num_images, (N*5)]
for _ in xrange(imdb.num_classes)
]
output_dir = get_output_dir(imdb, net)
# timers
_t = {'im_detect': Timer(), 'misc': Timer()}
if not cfg.TEST.HAS_RPN:
roidb = imdb.roidb
'''------- 对每一张图片依次操作 -------'''
for i in xrange(num_images):
# filter out any ground truth boxes
if cfg.TEST.HAS_RPN:
box_proposals = None
else:
# The roidb may contain ground-truth rois (for example, if the roidb
# comes from the training or val split). We only want to evaluate
# detection on the *non*-ground-truth rois. We select those the rois
# that have the gt_classes field set to 0, which means there's no
# ground truth.
box_proposals = roidb[i]['boxes'][roidb[i]['gt_classes'] == 0]
im = cv2.imread(imdb.image_path_at(i))
_t['im_detect'].tic()
'''------- 返回结构: scores(R, cls_num), boxes(R, 4 x cls_num) -------'''
scores, boxes = im_detect(net, im, box_proposals) # -- 检测的关键步骤 --
_t['im_detect'].toc()
_t['misc'].tic()
# skip j = 0, because it's the background class
for j in xrange(1, imdb.num_classes):
inds = np.where(scores[:, j] > thresh)[0]
cls_scores = scores[inds, j]
cls_boxes = boxes[inds, j * 4:(j + 1) * 4]
cls_dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \
.astype(np.float32, copy=False)
keep = nms(cls_dets, cfg.TEST.NMS) # 进行非最大值抑制(NMS)
cls_dets = cls_dets[keep, :]
if vis:
vis_detections(im, imdb.classes[j], cls_dets)
# 插入一个 (n,5) 的结构, 其中5元 tuple 是 (x1, y1, x2, y2, score), 为第i张图片中第j类的检测结果
all_boxes[j][i] = cls_dets
# Limit to max_per_image detections *over all classes*
if max_per_image > 0:
image_scores = np.hstack(
[all_boxes[j][i][:, -1] for j in xrange(1, imdb.num_classes)])
if len(image_scores) > max_per_image:
image_thresh = np.sort(image_scores)[-max_per_image]
for j in xrange(1, imdb.num_classes):
keep = np.where(all_boxes[j][i][:, -1] >= image_thresh)[0]
all_boxes[j][i] = all_boxes[j][i][keep, :]
_t['misc'].toc()
print 'im_detect: {:d}/{:d} {:.3f}s {:.3f}s' \
.format(i + 1, num_images, _t['im_detect'].average_time,
_t['misc'].average_time)
det_file = os.path.join(
output_dir,
'detections.pkl') # 貌似这个 detections.pkl 没有用到，仅用到了 all_boxes
with open(det_file, 'wb') as f:
cPickle.dump(all_boxes, f, cPickle.HIGHEST_PROTOCOL) # 存放检测文件
print 'Evaluating detections'
imdb.evaluate_detections(all_boxes, output_dir)
def demo(image_name, image_no, net):
colors = [
"blue", "green", "red", "cyan", "magenta", "yellow", "black", "white",
"darkblue", "orchid", "springgreen", "lime", "deepskyblue",
"mediumvioletred", "maroon", "orangered", "navy", "olive", "orange",
"orangered", "orchid", "pink", "plum", "purple", "salmon", "sienna",
"silver", "tan", "teal", "tomato", "violet", "wheat", "yellow",
"yellowgreen", "lavender", "palevioletred"
]
conf_thresh = 0.3
min_boxes = 36
max_boxes = 36
indexes = []
cfg.TEST.NMS = 0.6
im = cv2.imread(
os.path.join(
"/media/sadaf/e4da0f25-29be-4c9e-a432-3193ff5f5baf/Code/Pytorch_Code/transfer_learn/Analysis/targeted_attacks_ep16/adv_images",
image_name))
cls_append = []
scores, boxes, attr_scores, rel_scores = im_detect(net, im)
print(image_no)
# Keep the original boxes, don't worry about the regression bbox outputs
rois = net.blobs['rois'].data.copy()
# unscale back to raw image space
blobs, im_scales = _get_blobs(im, None)
cls_boxes = rois[:, 1:5] / im_scales[0]
cls_prob = net.blobs['cls_prob'].data
attr_prob = net.blobs['attr_prob'].data
pool5 = net.blobs['pool5_flat'].data
# Keep only the best detections
max_conf = np.zeros((rois.shape[0]))
for cls_ind in range(1, cls_prob.shape[1]):
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = np.array(nms(dets, cfg.TEST.NMS))
max_conf[keep] = np.where(cls_scores[keep] > max_conf[keep],
cls_scores[keep], max_conf[keep])
keep_boxes = np.where(max_conf >= conf_thresh)[0]
if len(keep_boxes) < min_boxes:
keep_boxes = np.argsort(max_conf)[::-1][:min_boxes]
elif len(keep_boxes) > max_boxes:
keep_boxes = np.argsort(max_conf)[::-1][:max_boxes]
############################
att_unique = np.unique(att_names[image_no * scale:(image_no * scale +
scale)])
att_unique_adv = np.unique(att_names_adv[image_no *
scale:(image_no * scale + scale)])
cls_unique = np.unique(att_cls[image_no * scale:(image_no * scale +
scale)])
cls_unique_adv = np.unique(att_cls_adv[image_no * scale:(image_no * scale +
scale)])
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
sizes = np.shape(im)
height = float(sizes[0])
width = float(sizes[1])
fig = plt.figure()
fig.set_size_inches(width / height, 1, forward=False)
ax = plt.Axes(fig, [0., 0., 1., 1.])
ax.set_axis_off()
fig.add_axes(ax)
plt.imshow(im)
#colors=["blue","green","red","cyan","magenta","yellow","black","white","darkblue","orchid","springgreen","lime","deepskyblue","mediumvioletred","maroon","orangered"]
boxes = cls_boxes[keep_boxes]
#print (boxes)
#print (keep_boxes)
objects = np.argmax(cls_prob[keep_boxes][:, 1:], axis=1)
attr_thresh = 0.1
attr = np.argmax(attr_prob[keep_boxes][:, 1:], axis=1)
attr_conf = np.max(attr_prob[keep_boxes][:, 1:], axis=1)
count_box = 0
for i in range(len(keep_boxes)):
bbox = boxes[i]
if bbox[0] == 0:
bbox[0] = 1
if bbox[1] == 0:
bbox[1] = 1
#cls = classes[objects[i]+1]
if attr_conf[i] > attr_thresh:
#for k in range (len(att_unique)):
# for l in range (len(cls_unique)):
#if attributes[attr[i]+1]==att_unique[k]:
# if classes[objects[i]+1] == cls_unique[l]:
#if attributes[attr[i]+1] not in att_unique_adv:
#if classes[objects[i]+1] not in cls_unique_adv:
if attributes[attr[i] + 1] in att_unique_adv:
if classes[objects[i] + 1] in cls_unique_adv:
cls = attributes[attr[i] + 1] + " " + classes[objects[i] +
1]
cls_append.append(cls)
count = cls_append.count(cls)
if count == 1:
count_box = count_box + 1
print(cls)
plt.gca().add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor=colors[i],
linewidth=0.3,
alpha=0.5))
plt.gca().text(bbox[0],
bbox[1] - 2,
'%s' % (cls),
bbox=dict(facecolor='blue',
alpha=0,
linewidth=0.2),
fontsize=2.5,
color=colors[i])
#plt.suptitle((correct_cls[int(image_no)])+ " "+(wrong_cls[int(image_no)]),fontsize=2)
plt.savefig(
'/media/sadaf/e4da0f25-29be-4c9e-a432-3193ff5f5baf/Code/Pytorch_Code/transfer_learn/Analysis/targeted_attacks_ep16/adv_bb1/adv_bb{}.jpg'
.format(image_no),
dpi=1500)
#plt.savefig('/media/sadaf/e4da0f25-29be-4c9e-a432-3193ff5f5baf/Code/Pytorch_Code/transfer_learn/Analysis/clean_bb/clean_bb{}_50.jpg'.format(image_no), dpi = 1500)
#plt.tight_layout()
plt.close()
def bag_demo_double(net, image_name, cat_ids, bboxes):
"""Detect object classes in an image using pre-computed object proposals."""
im = cv2.imread(image_name)
# im = url_to_image(image_name)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
# print scores
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
# Visualize detections for each class
im = im[:, :, (2, 1, 0)]
#######
fig = plt.figure()
ax1 = fig.add_subplot(1, 2, 1)
#######
colors = plt.cm.hsv(np.linspace(0, 1, len(CLASSES))).tolist()
ax1.imshow(im)
currentAxis = plt.gca()
CONF_THRESH = 0.6
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
keep_final = np.where(dets[:, 4] > CONF_THRESH)[0]
for i in keep_final:
xmin = dets[i, 0]
ymin = dets[i, 1]
xmax = dets[i, 2]
ymax = dets[i, 3]
score = dets[i, 4]
label_name = cls
display_txt = '%s: %.2f' % (label_name, score)
coords = (xmin, ymin), xmax - xmin + 1, ymax - ymin + 1
color = colors[cls_ind]
currentAxis.add_patch(
plt.Rectangle(*coords,
fill=False,
edgecolor=color,
linewidth=2))
currentAxis.text(xmin,
ymin,
display_txt,
bbox={
'facecolor': color,
'alpha': 0.5
})
ax2 = fig.add_subplot(1, 2, 2)
ax2.imshow(im)
currentAxis = plt.gca()
for idx, cls_ind in enumerate(cat_ids):
cls = CLASSES[cls_ind]
xmin = bboxes[idx, 0]
ymin = bboxes[idx, 1]
xmax = xmin + bboxes[idx, 2] - 1
ymax = ymin + bboxes[idx, 3] - 1
label_name = cls
display_txt = '%s: %.2f' % (label_name, 1)
coords = (xmin, ymin), xmax - xmin + 1, ymax - ymin + 1
color = colors[cls_ind]
currentAxis.add_patch(
plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
currentAxis.text(xmin,
ymin,
display_txt,
bbox={
'facecolor': color,
'alpha': 0.5
})
plt.show()
def demo(net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
#print image_name
#print '\n\n\n\n'
# Load the demo image
#im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)
#im_file = os.path.join('/tmp/caffe_demos_uploads/', image_name,'.png')
#im=image_name
#print im_file
im = cv2.imread(image_name)
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(net, im)
timer.toc()
print('Detection took {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
print boxes.shape
print scores.shape
# Visualize detections for each class
timer = Timer()
timer.tic()
CONF_THRESH = 0.8
NMS_THRESH = 0.3
im = im[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
#print('111111111111111111111\n\n\n')
#vis_detections(im, cls, dets, thresh=CONF_THRESH,)
# print('jnjnjnjjnjnjjnjjjjjjjjjj\n\n\n')
#####################################################
class_name = cls
inds = np.where(dets[:, -1] >= 0.8)[0]
# print('begin\n\n\n')
if len(inds) == 0:
continue
# print('123\n\n\n')
#im = im[:, :, (2, 1, 0)]
#fig, ax = plt.subplots(figsize=(12, 12))
#ax.imshow(im, aspect='equal')
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1],
fill=False,
edgecolor='red',
linewidth=3.5))
ax.text(
bbox[0],
bbox[1] - 2,
#'{:s} {:.3f}'.format(class_name, score),
class_name,
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14,
color='white',
fontproperties=myfont)
#ax.set_title(('{} detections with '
# 'p({} | box) >= {:.1f}').format(class_name, class_name,
# thresh),
# fontsize=14)
plt.axis('off')
plt.tight_layout()
plt.draw()
# print('end\n\n\n')
#ax.imwrite('123.jpg')
plt.savefig(image_name, dpi=400, bbox_inches="tight")
timer.toc()
print('draw time {:.3f}s for '
'{:d} object proposals').format(timer.total_time, boxes.shape[0])
def proposal_layer(rpn_cls_prob_reshape,
rpn_bbox_pred,
im_info,
cfg_key,
_feat_stride=[
16,
],
anchor_scales=[8, 16, 32]):
# Algorithm:
#
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
#layer_params = yaml.load(self.param_str_)
_anchors = generate_anchors(
scales=np.array(anchor_scales)) # return 9*4 anchors coordinates
_num_anchors = _anchors.shape[0]
rpn_cls_prob_reshape = np.transpose(
rpn_cls_prob_reshape,
[0, 3, 1, 2]) #2 score values after softmax------(1,18,14,14)
rpn_bbox_pred = np.transpose(rpn_bbox_pred,
[0, 3, 1, 2]) #------(1,36,14,14)
#rpn_cls_prob_reshape = np.transpose(np.reshape(rpn_cls_prob_reshape,[1,rpn_cls_prob_reshape.shape[0],rpn_cls_prob_reshape.shape[1],rpn_cls_prob_reshape.shape[2]]),[0,3,2,1])
#rpn_bbox_pred = np.transpose(rpn_bbox_pred,[0,3,2,1])
im_info = im_info[0] # [max_length, max_width, im_scale]
assert rpn_cls_prob_reshape.shape[0] == 1, \
'Only single item batches are supported'
# cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
#cfg_key = 'TEST'
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
#12000 Number of top scoring boxes to keep before apply NMS to RPN proposals
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
#2000 Number of top scoring boxes to keep after applying NMS to RPN proposals
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
#0.7 NMS threshold used on RPN proposals
min_size = cfg[cfg_key].RPN_MIN_SIZE
#16 Proposal height and width both need to be greater than RPN_MIN_SIZE (at orig image scale)
# the first set of _num_anchors channels are bg probs(background)
# the second set are the fg probs, which we want
scores = rpn_cls_prob_reshape[:, _num_anchors:, :, :] #-------(1,9,14,14)
bbox_deltas = rpn_bbox_pred
#im_info = bottom[2].data[0, :]
if DEBUG:
print 'im_size: ({}, {})'.format(im_info[0], im_info[1])
print 'scale: {}'.format(im_info[2])
# 1. Generate proposals from bbox deltas and shifted anchors
height, width = scores.shape[-2:] #14; 14
if DEBUG: # false
print 'score map size: {}'.format(scores.shape)
# Enumerate all shifts
shift_x = np.arange(
0, width
) * _feat_stride #array([ 0, 16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208])
shift_y = np.arange(
0, height
) * _feat_stride #array([ 0, 16, 32, 48, 64, 80, 96, 112, 128, 144, 160, 176, 192, 208])
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(),
shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = _num_anchors
K = shifts.shape[0]
anchors = _anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
"""
return[196,9,4]
array([[[ -83., -39., 100., 56.],
[-175., -87., 192., 104.],
[-359., -183., 376., 200.],
...,
[ -35., -79., 52., 96.],
[ -79., -167., 96., 184.],
[-167., -343., 184., 360.]],
[[ -67., -39., 116., 56.],
[-159., -87., 208., 104.],
[-343., -183., 392., 200.],
...,
[ -19., -79., 68., 96.],
[ -63., -167., 112., 184.],
[-151., -343., 200., 360.]],
[[ -51., -39., 132., 56.],
[-143., -87., 224., 104.],
[-327., -183., 408., 200.],
...,
[ -3., -79., 84., 96.],
[ -47., -167., 128., 184.],
[-135., -343., 216., 360.]],
...,
[[ 93., 169., 276., 264.],
[ 1., 121., 368., 312.],
[-183., 25., 552., 408.],
...,
[ 141., 129., 228., 304.],
[ 97., 41., 272., 392.],
[ 9., -135., 360., 568.]],
[[ 109., 169., 292., 264.],
[ 17., 121., 384., 312.],
[-167., 25., 568., 408.],
...,
[ 157., 129., 244., 304.],
[ 113., 41., 288., 392.],
[ 25., -135., 376., 568.]],
[[ 125., 169., 308., 264.],
[ 33., 121., 400., 312.],
[-151., 25., 584., 408.],
...,
[ 173., 129., 260., 304.],
[ 129., 41., 304., 392.],
[ 41., -135., 392., 568.]]])"""
anchors = anchors.reshape((K * A, 4)) #(14*14*9, 4)
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) (1,36,14,14)format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas = bbox_deltas.transpose((0, 2, 3, 1)).reshape(
(-1, 4)) # (1,36,14,14)-----(1,14,14,36)------(14*14*9,4)
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores = scores.transpose((0, 2, 3, 1)).reshape(
(-1, 1)) # (1,9,14,14)-----(1,14,14,9)-----(14*14*9,1)
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas)
# generate the prodicted box（x1,y1,x2,y2） maped in original image (14*14*9, 4), only inside anchors,
# 因为bbox_deltas(dx,dy,dw,dh)是相对于相应anchors（x1,y1,x2,y2）的偏移
# 2. clip predicted boxes to image boundaries
proposals = clip_boxes(proposals, im_info[:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size * im_info[2])
#min_size:16; im_info[2]: max_width; """Remove all boxes with any side smaller than min_size. return index
proposals = proposals[keep, :] #len(keep)*4
scores = scores[keep] #len(keep)
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores.ravel().argsort()[::-1]
if pre_nms_topN > 0:
#12000
order = order[:pre_nms_topN]
proposals = proposals[order, :] #len(order)*4
scores = scores[order] #len(order)
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores)), nms_thresh)
# nms_thresh: 0.7
if post_nms_topN > 0:
#2000
keep = keep[:post_nms_topN]
proposals = proposals[keep, :] #<=2000
scores = scores[keep]
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1),
dtype=np.float32) #(2000, 1)
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
return blob
def forward(self, bottom, top):
# Algorithm:
#
# for each (H, W) location i
# generate A anchor boxes centered on cell i
# apply predicted bbox deltas at cell i to each of the A anchors
# clip predicted boxes to image
# remove predicted boxes with either height or width < threshold
# sort all (proposal, score) pairs by score from highest to lowest
# take top pre_nms_topN proposals before NMS
# apply NMS with threshold 0.7 to remaining proposals
# take after_nms_topN proposals after NMS
# return the top proposals (-> RoIs top, scores top)
#assert bottom[0].data.shape[0] == 1, \
# 'Only single item batches are supported'
#print 'Configuration check here!';
cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N
post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
nms_thresh = cfg[cfg_key].RPN_NMS_THRESH
min_size = cfg[cfg_key].RPN_MIN_SIZE
try:
num_imgs = cfg[cfg_key].IMS_PER_BATCH
except:
num_imgs = 1;
#post_nms_topN = int(post_nms_topN/num_imgs)
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs, which we want
scores = bottom[0].data[:, self._num_anchors:, :, :]
bbox_deltas = bottom[1].data
im_info = bottom[2].data
#im_info = bottom[2].data[0, :]
if DEBUG:
print 'im_size: ({}, {})'.format(im_info[0][0], im_info[0][1])
print 'scale: {}'.format(im_info[0][2])
# 1. Generate proposals from bbox deltas and shifted anchors
height, width = scores.shape[-2:]
if DEBUG:
print 'score map size: {}'.format(scores.shape)
# Enumerate all shifts
shift_x = np.arange(0, width) * self._feat_stride
shift_y = np.arange(0, height) * self._feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(),
shift_x.ravel(), shift_y.ravel())).transpose()
# Enumerate all shifted anchors:
#
# add A anchors (1, A, 4) to
# cell K shifts (K, 1, 4) to get
# shift anchors (K, A, 4)
# reshape to (K*A, 4) shifted anchors
A = self._num_anchors
K = shifts.shape[0]
anchors = self._anchors.reshape((1, A, 4)) + \
shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4))
blob_all = [];
#diggy: start
#print 'Check the entire loop';
for im_i in range(num_imgs):
# Transpose and reshape predicted bbox transformations to get them
# into the same order as the anchors:
#
# bbox deltas will be (1, 4 * A, H, W) format
# transpose to (1, H, W, 4 * A)
# reshape to (1 * H * W * A, 4) where rows are ordered by (h, w, a)
# in slowest to fastest order
bbox_deltas_i = bbox_deltas[im_i];
bbox_deltas_i = bbox_deltas_i.reshape(1,bbox_deltas_i.shape[0],bbox_deltas_i.shape[1],bbox_deltas_i.shape[2]);
bbox_deltas_i = bbox_deltas_i.transpose((0, 2, 3, 1)).reshape((-1, 4))
# Same story for the scores:
#
# scores are (1, A, H, W) format
# transpose to (1, H, W, A)
# reshape to (1 * H * W * A, 1) where rows are ordered by (h, w, a)
scores_i = scores[im_i];
scores_i = scores_i.reshape(1,scores_i.shape[0],scores_i.shape[1],scores_i.shape[2]);
scores_i = scores_i.transpose((0, 2, 3, 1)).reshape((-1, 1))
# Convert anchors into proposals via bbox transformations
proposals = bbox_transform_inv(anchors, bbox_deltas_i)
# 2. clip predicted boxes to image
proposals = clip_boxes(proposals, im_info[im_i][:2])
# 3. remove predicted boxes with either height or width < threshold
# (NOTE: convert min_size to input image scale stored in im_info[2])
keep = _filter_boxes(proposals, min_size * im_info[im_i][2])
proposals = proposals[keep, :]
scores_i = scores_i[keep]
# 4. sort all (proposal, score) pairs by score from highest to lowest
# 5. take top pre_nms_topN (e.g. 6000)
order = scores_i.ravel().argsort()[::-1]
if pre_nms_topN > 0:
order = order[:pre_nms_topN]
proposals = proposals[order, :]
scores_i = scores_i[order]
# 6. apply nms (e.g. threshold = 0.7)
# 7. take after_nms_topN (e.g. 300)
# 8. return the top proposals (-> RoIs top)
keep = nms(np.hstack((proposals, scores_i)), nms_thresh)
if post_nms_topN > 0:
keep = keep[:post_nms_topN]
proposals = proposals[keep, :]
scores_i = scores_i[keep]
# Output rois blob
# Our RPN implementation only supports a single input image, so all
# batch inds are 0
batch_inds = np.zeros((proposals.shape[0], 1), dtype=np.float32)
batch_inds[:] = im_i;
blob = np.hstack((batch_inds, proposals.astype(np.float32, copy=False)))
#print 'Blob copying check';
if blob_all == [] and blob != []:
blob_all = blob.copy();
else:
if blob != []:
blob_all = np.vstack((blob_all,blob));
#diggy: end
#print 'Proposal layer: Dimensionality check';
top[0].reshape(*(blob_all.shape))
top[0].data[...] = blob_all
# [Optional] output scores blob
if len(top) > 1:
top[1].reshape(*(scores_i.shape))
top[1].data[...] = scores_i
